Road Friction Coefficient Real-Time Identification Based on the Tire Dynamic Friction Model

Road friction coefficient real-time estimation methods is an important issue and problem in automotive active safety con- trol system development. First a fixed feedback gain sliding mode observer of road adhesion coefficient is designed through the es-tablishment of tire/road dynamic friction model in this article. The simulation results shows that the observer can well real-time iden-tify the current road adhesion characteristics. And more importantly, the observer only need wheel speed signal and the braking torque （brake pressure） signal, so the system is low cost, and its adaptability is good. There is no doubt this estimation method has a good application prospect.     

基于信息融合技术的车辆行驶状态估计

针对车辆行驶状态估计问题,基于扩展卡尔曼滤波(EKF)理论设计了汽车横摆角速度、质心侧偏角和纵向车速的估计算法.建立了车辆非线性三自由度估算模型,应用扩展卡尔曼滤波对纵向加速度、侧向加速度和方向盘转角低成本传感器信号的信息融合,实现对车辆行驶状态的准确估计.选择高速双移线工况,通过CarSim与Matlab/Simulink联合仿真对算法进行仿真验证.结果表明:算法能够准确估计车辆的横摆角速度、质心侧偏角和纵向车速,并具有良好的实时性.     

Vehicle Dynamic State Estimation: State of the Art Schemes and Perspectives

Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.      

基于扩展卡尔曼滤波器的AUV导航定位数据融合

导航定位是AUV(自主式水下潜器)自动化受限的关键技术.GPS等单独的定位方式又存在缺陷.故采用低成本罗经-DVL-GPS组合导航定位.因为导航定位观测的非线性关系,设计了基于扩展卡尔曼滤波器的导航定位数据融合算法.针对扩展卡尔曼滤波器存在的滤波发散现象,引入机动目标模型抑制滤波发散,研究并仿真了AUV的两种运动方式,实现了覆盖四个象限运动的导航定位仿真,结果表明算法能有效进行数据融合,提高状态估计精确度.     

基于动力学模型的汽车制动稳定性虚拟展示

现有3D引擎的物理模型不能真实反映车辆制动时的运动状态。为此,提出一种汽车制动稳定性虚拟展示系统。建立汽车动力学模型,包括四轮车辆模型和车轮轮胎模型,利用上层仿真软件对动力学模型进行运算和虚拟场景渲染,给出车轮状态和虚拟仪表的展现方法,并基于3D引擎设计虚拟展现系统。实验结果表明,该系统能同时观测整车及车轮的运行状态,实时再现制动过程中车轮抱死、车身横摆侧滑的行为,其动力学模型能够满足虚拟展现对画面渲染的实时性、连续性要求。     

动态补偿逆的非线性内模控制在机器人中的应用

针对机器人的非线性不确定性和传统非线性内模控制在控制上存在的不足．提出一种基于动态补偿逆的非线性不确定系统RBF内模控制,在引入RBF建立逆模型的同时．将无模型自适应控制方法作为附加控制器,用于在模型偏离被控对象时在线修正逆模型。仿真结果表明,本文提出的方法不仅对机器人系统的常量摄动具有较好的鲁棒性,对时变不确定性仍能保持较好的跟踪效果．具有较好的实时性、鲁棒性和在线校正功能。     

Performance Improvement of Fuel Cells Using Perturbation‐Based Extremum Seeking and Model Reference Adaptive Control

Nowadays, fuel cells (FCs) are considered suitable alternative sources for electrical energy applications. One major challenge encountered in FCs is relevant to the performance of the maximum power point tracking (MPPT) under FC parameter changes and load variations. This challenge is due to the nonlinearity and time‐varying dynamics of FC systems. In this paper, the MPPT is studied in a system composed of a FC and a DC‐DC converter. To improve the performance of the MPPT, application of perturbation‐based extremum seeking (PES) and model reference adaptive control (MRAC) is proposed. This control scheme can efficiently handle the uncertainties in the FC as well as the load, through two control levels. The first level is PES utilized to adjust the duty cycle of the DC‐DC converter; and the second level is MRAC employed to achieve the desired dynamic response. Using the proposed control strategy, design and analysis of the control levels can be realized independently, which results in easy implementation. This is achieved due to considerable differences between the time constants of the control levels. The simulation results are utilized to confirm the effectiveness of the proposed scheme in response to the variations of FC parameters and load. Also, comparative studies with a combination of PES and PID controller are provided in the simulation.